Enhancement of the survival probability of a photovoltaic converter–An optimization approach. (July 2018)

• Record Type: Journal Article
• Authors: de León-Aldaco, Susana; Calleja, Hugo; Aguayo, Jesús; Correa, Carlos; Flores, Eligio
• Is Part Of: Computers & electrical engineering. Volume 69(2018)
• Page Start: 115
• Page End: 128
• Publication Date: 2018-07
• Subjects: Power converter -- Genetic algorithms -- Reliability estimation -- Optimization methods -- Reliability -- Circuit optimization -- Circuit reliability -- FIDES methodology -- Failure rate -- Thermal factor; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.05.017
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Maximizing the return on investment of a photovoltaic array requires a power electronics converter with the longest possible operational life. This paper is aimed at presenting a heatsink optimization approach to enhance the survival probability of a high-efficiency 1 kW DC/DC converter, specifically designed for PV applications. A main feature of the enhancement is that it takes into account the meteorological characteristics of the intended installation site: a coastal area with high humidity and cloudiness throughout the year. As a first step, the converter reliability is estimated according to the FIDES procedure, yielding a mean time between failures MTBF = 98.15 FIT. The numerical results pinpoint the diodes as the most failure-prone components, and temperature as the dominant stressor, clearly suggesting that the performance can be improved by resorting to better heatsinks. As a second step, a heatsink optimization problem is established, involving three objective functions: minimization of heatsink thermal resistance and weight, and maximization of the cooling system performance index, The problem is solved using a genetic algorithm, which yields MTBF = 74.09 FIT. The approach takes advantage of the computer tools currently available: mathematical toolboxes to implement the optimization algorithm, user-friendly reliability prediction software, and software packages capable of simulating the thermal behavior of heatsinks with good accuracy.